Overview

Brought to you by YData

Dataset statistics

Number of variables25
Number of observations36992
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.1 MiB
Average record size in memory200.0 B

Variable types

Text5
Numeric6
Categorical9
DateTime2
Boolean3

Alerts

churn_risk_score is highly overall correlated with points_in_walletHigh correlation
complaint_status is highly overall correlated with past_complaintHigh correlation
offer_application_preference is highly overall correlated with used_special_discountHigh correlation
past_complaint is highly overall correlated with complaint_statusHigh correlation
points_in_wallet is highly overall correlated with churn_risk_scoreHigh correlation
used_special_discount is highly overall correlated with offer_application_preferenceHigh correlation
customer_id has unique valuesUnique
Name has unique valuesUnique
security_no has unique valuesUnique

Reproduction

Analysis started2025-10-25 19:07:59.225478
Analysis finished2025-10-25 19:08:34.364199
Duration35.14 seconds
Software versionydata-profiling vv4.17.0
Download configurationconfig.json

Variables

customer_id
Text

Unique 

Distinct36992
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size289.1 KiB
2025-10-26T00:38:34.981125image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length36
Median length36
Mean length35.271734
Min length20

Characters and Unicode

Total characters1304772
Distinct characters12
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique36992 ?
Unique (%)100.0%

Sample

1st rowfffe4300490044003600300030003800
2nd rowfffe43004900440032003100300035003700
3rd rowfffe4300490044003100390032003600
4th rowfffe43004900440036003000330031003600
5th rowfffe43004900440031003900350030003600
ValueCountFrequency (%)
fffe43004900440036003000300038001
 
< 0.1%
fffe430049004400330037003300380039001
 
< 0.1%
fffe430049004400320032003500330032001
 
< 0.1%
fffe430049004400330033003300320032001
 
< 0.1%
fffe43004900440031003900320036001
 
< 0.1%
fffe430049004400360030003300310036001
 
< 0.1%
fffe430049004400310039003500300036001
 
< 0.1%
fffe430049004400360033003200350033001
 
< 0.1%
fffe430049004400310031003600370039001
 
< 0.1%
fffe43004900440038003000350038001
 
< 0.1%
Other values (36982)36982
> 99.9%
2025-10-26T00:38:35.905725image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0592862
45.4%
3236016
 
18.1%
4168252
 
12.9%
f110976
 
8.5%
951579
 
4.0%
e36992
 
2.8%
120900
 
1.6%
220831
 
1.6%
520484
 
1.6%
616674
 
1.3%
Other values (2)29206
 
2.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1156804
88.7%
Lowercase Letter147968
 
11.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0592862
51.2%
3236016
 
20.4%
4168252
 
14.5%
951579
 
4.5%
120900
 
1.8%
220831
 
1.8%
520484
 
1.8%
616674
 
1.4%
814641
 
1.3%
714565
 
1.3%
Lowercase Letter
ValueCountFrequency (%)
f110976
75.0%
e36992
 
25.0%

Most occurring scripts

ValueCountFrequency (%)
Common1156804
88.7%
Latin147968
 
11.3%

Most frequent character per script

Common
ValueCountFrequency (%)
0592862
51.2%
3236016
 
20.4%
4168252
 
14.5%
951579
 
4.5%
120900
 
1.8%
220831
 
1.8%
520484
 
1.8%
616674
 
1.4%
814641
 
1.3%
714565
 
1.3%
Latin
ValueCountFrequency (%)
f110976
75.0%
e36992
 
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1304772
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0592862
45.4%
3236016
 
18.1%
4168252
 
12.9%
f110976
 
8.5%
951579
 
4.0%
e36992
 
2.8%
120900
 
1.6%
220831
 
1.6%
520484
 
1.6%
616674
 
1.3%
Other values (2)29206
 
2.2%

Name
Text

Unique 

Distinct36992
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size289.1 KiB
2025-10-26T00:38:36.565333image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length24
Median length21
Mean length13.522924
Min length6

Characters and Unicode

Total characters500240
Distinct characters53
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique36992 ?
Unique (%)100.0%

Sample

1st rowPattie Morrisey
2nd rowTraci Peery
3rd rowMerideth Mcmeen
4th rowEufemia Cardwell
5th rowMeghan Kosak
ValueCountFrequency (%)
sidney137
 
0.2%
gilda126
 
0.2%
selena123
 
0.2%
noe123
 
0.2%
lesli121
 
0.2%
karri121
 
0.2%
marietta120
 
0.2%
kenny120
 
0.2%
earlie119
 
0.2%
dean119
 
0.2%
Other values (2511)72755
98.3%
2025-10-26T00:38:37.597285image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e54599
 
10.9%
a50451
 
10.1%
36992
 
7.4%
n36286
 
7.3%
r34094
 
6.8%
i33029
 
6.6%
l29840
 
6.0%
o24859
 
5.0%
t17988
 
3.6%
s15985
 
3.2%
Other values (43)166117
33.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter389264
77.8%
Uppercase Letter73984
 
14.8%
Space Separator36992
 
7.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e54599
14.0%
a50451
13.0%
n36286
9.3%
r34094
8.8%
i33029
8.5%
l29840
 
7.7%
o24859
 
6.4%
t17988
 
4.6%
s15985
 
4.1%
d11744
 
3.0%
Other values (16)80389
20.7%
Uppercase Letter
ValueCountFrequency (%)
S7104
 
9.6%
M6774
 
9.2%
L5702
 
7.7%
B4974
 
6.7%
A4941
 
6.7%
C4690
 
6.3%
D3984
 
5.4%
K3882
 
5.2%
G3507
 
4.7%
R3447
 
4.7%
Other values (16)24979
33.8%
Space Separator
ValueCountFrequency (%)
36992
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin463248
92.6%
Common36992
 
7.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
e54599
 
11.8%
a50451
 
10.9%
n36286
 
7.8%
r34094
 
7.4%
i33029
 
7.1%
l29840
 
6.4%
o24859
 
5.4%
t17988
 
3.9%
s15985
 
3.5%
d11744
 
2.5%
Other values (42)154373
33.3%
Common
ValueCountFrequency (%)
36992
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII500240
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e54599
 
10.9%
a50451
 
10.1%
36992
 
7.4%
n36286
 
7.3%
r34094
 
6.8%
i33029
 
6.6%
l29840
 
6.0%
o24859
 
5.0%
t17988
 
3.6%
s15985
 
3.2%
Other values (43)166117
33.2%

age
Real number (ℝ)

Distinct55
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.118161
Minimum10
Maximum64
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size289.1 KiB
2025-10-26T00:38:37.946153image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile12
Q123
median37
Q351
95-th percentile62
Maximum64
Range54
Interquartile range (IQR)28

Descriptive statistics

Standard deviation15.867412
Coefficient of variation (CV)0.4274838
Kurtosis-1.1987327
Mean37.118161
Median Absolute Deviation (MAD)14
Skewness-0.0073193193
Sum1373075
Variance251.77477
MonotonicityNot monotonic
2025-10-26T00:38:38.341225image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
33720
 
1.9%
42716
 
1.9%
16716
 
1.9%
38714
 
1.9%
30711
 
1.9%
61709
 
1.9%
60704
 
1.9%
57704
 
1.9%
41699
 
1.9%
59696
 
1.9%
Other values (45)29903
80.8%
ValueCountFrequency (%)
10670
1.8%
11654
1.8%
12661
1.8%
13654
1.8%
14670
1.8%
15649
1.8%
16716
1.9%
17683
1.8%
18629
1.7%
19660
1.8%
ValueCountFrequency (%)
64672
1.8%
63656
1.8%
62677
1.8%
61709
1.9%
60704
1.9%
59696
1.9%
58678
1.8%
57704
1.9%
56682
1.8%
55695
1.9%

gender
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size289.1 KiB
F
18490 
M
18443 
Unknown
 
59

Length

Max length7
Median length1
Mean length1.0095696
Min length1

Characters and Unicode

Total characters37346
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowF
2nd rowF
3rd rowF
4th rowM
5th rowF

Common Values

ValueCountFrequency (%)
F18490
50.0%
M18443
49.9%
Unknown59
 
0.2%

Length

2025-10-26T00:38:38.707680image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-26T00:38:38.993983image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
f18490
50.0%
m18443
49.9%
unknown59
 
0.2%

Most occurring characters

ValueCountFrequency (%)
F18490
49.5%
M18443
49.4%
n177
 
0.5%
U59
 
0.2%
k59
 
0.2%
o59
 
0.2%
w59
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter36992
99.1%
Lowercase Letter354
 
0.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n177
50.0%
k59
 
16.7%
o59
 
16.7%
w59
 
16.7%
Uppercase Letter
ValueCountFrequency (%)
F18490
50.0%
M18443
49.9%
U59
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Latin37346
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
F18490
49.5%
M18443
49.4%
n177
 
0.5%
U59
 
0.2%
k59
 
0.2%
o59
 
0.2%
w59
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII37346
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
F18490
49.5%
M18443
49.4%
n177
 
0.5%
U59
 
0.2%
k59
 
0.2%
o59
 
0.2%
w59
 
0.2%

security_no
Text

Unique 

Distinct36992
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size289.1 KiB
2025-10-26T00:38:39.699635image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters258944
Distinct characters36
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique36992 ?
Unique (%)100.0%

Sample

1st rowXW0DQ7H
2nd row5K0N3X1
3rd row1F2TCL3
4th rowVJGJ33N
5th rowSVZXCWB
ValueCountFrequency (%)
xw0dq7h1
 
< 0.1%
6rz86vw1
 
< 0.1%
c229qzz1
 
< 0.1%
0481qnq1
 
< 0.1%
1f2tcl31
 
< 0.1%
vjgj33n1
 
< 0.1%
svzxcwb1
 
< 0.1%
psg1lgf1
 
< 0.1%
r3cx1ea1
 
< 0.1%
4uj15511
 
< 0.1%
Other values (36982)36982
> 99.9%
2025-10-26T00:38:40.743258image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
G7410
 
2.9%
D7391
 
2.9%
H7346
 
2.8%
Z7307
 
2.8%
A7294
 
2.8%
T7270
 
2.8%
F7262
 
2.8%
R7249
 
2.8%
M7248
 
2.8%
57244
 
2.8%
Other values (26)185923
71.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter187293
72.3%
Decimal Number71651
 
27.7%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
G7410
 
4.0%
D7391
 
3.9%
H7346
 
3.9%
Z7307
 
3.9%
A7294
 
3.9%
T7270
 
3.9%
F7262
 
3.9%
R7249
 
3.9%
M7248
 
3.9%
O7229
 
3.9%
Other values (16)114287
61.0%
Decimal Number
ValueCountFrequency (%)
57244
10.1%
27236
10.1%
77220
10.1%
87213
10.1%
97171
10.0%
47171
10.0%
17163
10.0%
07157
10.0%
37043
9.8%
67033
9.8%

Most occurring scripts

ValueCountFrequency (%)
Latin187293
72.3%
Common71651
 
27.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
G7410
 
4.0%
D7391
 
3.9%
H7346
 
3.9%
Z7307
 
3.9%
A7294
 
3.9%
T7270
 
3.9%
F7262
 
3.9%
R7249
 
3.9%
M7248
 
3.9%
O7229
 
3.9%
Other values (16)114287
61.0%
Common
ValueCountFrequency (%)
57244
10.1%
27236
10.1%
77220
10.1%
87213
10.1%
97171
10.0%
47171
10.0%
17163
10.0%
07157
10.0%
37043
9.8%
67033
9.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII258944
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
G7410
 
2.9%
D7391
 
2.9%
H7346
 
2.8%
Z7307
 
2.8%
A7294
 
2.8%
T7270
 
2.8%
F7262
 
2.8%
R7249
 
2.8%
M7248
 
2.8%
57244
 
2.8%
Other values (26)185923
71.8%

region_category
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size289.1 KiB
Town
19556 
City
12737 
Village
4699 

Length

Max length7
Median length4
Mean length4.3810824
Min length4

Characters and Unicode

Total characters162065
Distinct characters13
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowVillage
2nd rowCity
3rd rowTown
4th rowCity
5th rowCity

Common Values

ValueCountFrequency (%)
Town19556
52.9%
City12737
34.4%
Village4699
 
12.7%

Length

2025-10-26T00:38:41.159837image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-26T00:38:41.453007image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
town19556
52.9%
city12737
34.4%
village4699
 
12.7%

Most occurring characters

ValueCountFrequency (%)
T19556
12.1%
o19556
12.1%
w19556
12.1%
n19556
12.1%
i17436
10.8%
C12737
7.9%
t12737
7.9%
y12737
7.9%
l9398
5.8%
V4699
 
2.9%
Other values (3)14097
8.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter125073
77.2%
Uppercase Letter36992
 
22.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o19556
15.6%
w19556
15.6%
n19556
15.6%
i17436
13.9%
t12737
10.2%
y12737
10.2%
l9398
7.5%
a4699
 
3.8%
g4699
 
3.8%
e4699
 
3.8%
Uppercase Letter
ValueCountFrequency (%)
T19556
52.9%
C12737
34.4%
V4699
 
12.7%

Most occurring scripts

ValueCountFrequency (%)
Latin162065
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
T19556
12.1%
o19556
12.1%
w19556
12.1%
n19556
12.1%
i17436
10.8%
C12737
7.9%
t12737
7.9%
y12737
7.9%
l9398
5.8%
V4699
 
2.9%
Other values (3)14097
8.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII162065
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
T19556
12.1%
o19556
12.1%
w19556
12.1%
n19556
12.1%
i17436
10.8%
C12737
7.9%
t12737
7.9%
y12737
7.9%
l9398
5.8%
V4699
 
2.9%
Other values (3)14097
8.7%
Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size289.1 KiB
Basic Membership
7724 
No Membership
7692 
Gold Membership
6795 
Silver Membership
5988 
Premium Membership
4455 

Length

Max length19
Median length17
Mean length15.947043
Min length13

Characters and Unicode

Total characters589913
Distinct characters24
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPlatinum Membership
2nd rowPremium Membership
3rd rowNo Membership
4th rowNo Membership
5th rowNo Membership

Common Values

ValueCountFrequency (%)
Basic Membership7724
20.9%
No Membership7692
20.8%
Gold Membership6795
18.4%
Silver Membership5988
16.2%
Premium Membership4455
12.0%
Platinum Membership4338
11.7%

Length

2025-10-26T00:38:41.757251image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-26T00:38:42.080822image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
membership36992
50.0%
basic7724
 
10.4%
no7692
 
10.4%
gold6795
 
9.2%
silver5988
 
8.1%
premium4455
 
6.0%
platinum4338
 
5.9%

Most occurring characters

ValueCountFrequency (%)
e84427
14.3%
i59497
10.1%
m50240
8.5%
r47435
 
8.0%
s44716
 
7.6%
p36992
 
6.3%
36992
 
6.3%
M36992
 
6.3%
b36992
 
6.3%
h36992
 
6.3%
Other values (14)118638
20.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter478937
81.2%
Uppercase Letter73984
 
12.5%
Space Separator36992
 
6.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e84427
17.6%
i59497
12.4%
m50240
10.5%
r47435
9.9%
s44716
9.3%
p36992
7.7%
b36992
7.7%
h36992
7.7%
l17121
 
3.6%
o14487
 
3.0%
Other values (7)50038
10.4%
Uppercase Letter
ValueCountFrequency (%)
M36992
50.0%
P8793
 
11.9%
B7724
 
10.4%
N7692
 
10.4%
G6795
 
9.2%
S5988
 
8.1%
Space Separator
ValueCountFrequency (%)
36992
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin552921
93.7%
Common36992
 
6.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
e84427
15.3%
i59497
10.8%
m50240
9.1%
r47435
8.6%
s44716
8.1%
p36992
 
6.7%
M36992
 
6.7%
b36992
 
6.7%
h36992
 
6.7%
l17121
 
3.1%
Other values (13)101517
18.4%
Common
ValueCountFrequency (%)
36992
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII589913
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e84427
14.3%
i59497
10.1%
m50240
8.5%
r47435
 
8.0%
s44716
 
7.6%
p36992
 
6.3%
36992
 
6.3%
M36992
 
6.3%
b36992
 
6.3%
h36992
 
6.3%
Other values (14)118638
20.1%
Distinct1096
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size289.1 KiB
Minimum2015-01-01 00:00:00
Maximum2017-12-31 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-10-26T00:38:42.478072image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-26T00:38:42.844553image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size289.1 KiB
No
15839 
Yes
15715 
?
5438 

Length

Max length3
Median length2
Mean length2.2778168
Min length1

Characters and Unicode

Total characters84261
Distinct characters6
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd row?
3rd rowYes
4th rowYes
5th rowNo

Common Values

ValueCountFrequency (%)
No15839
42.8%
Yes15715
42.5%
?5438
 
14.7%

Length

2025-10-26T00:38:43.291051image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-26T00:38:43.754281image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
no15839
42.8%
yes15715
42.5%
5438
 
14.7%

Most occurring characters

ValueCountFrequency (%)
N15839
18.8%
o15839
18.8%
Y15715
18.7%
e15715
18.7%
s15715
18.7%
?5438
 
6.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter47269
56.1%
Uppercase Letter31554
37.4%
Other Punctuation5438
 
6.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o15839
33.5%
e15715
33.2%
s15715
33.2%
Uppercase Letter
ValueCountFrequency (%)
N15839
50.2%
Y15715
49.8%
Other Punctuation
ValueCountFrequency (%)
?5438
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin78823
93.5%
Common5438
 
6.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
N15839
20.1%
o15839
20.1%
Y15715
19.9%
e15715
19.9%
s15715
19.9%
Common
ValueCountFrequency (%)
?5438
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII84261
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N15839
18.8%
o15839
18.8%
Y15715
18.7%
e15715
18.7%
s15715
18.7%
?5438
 
6.5%
Distinct11359
Distinct (%)30.7%
Missing0
Missing (%)0.0%
Memory size289.1 KiB
2025-10-26T00:38:44.589453image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length11
Median length8
Mean length7.9114944
Min length4

Characters and Unicode

Total characters292662
Distinct characters22
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6801 ?
Unique (%)18.4%

Sample

1st rowxxxxxxxx
2nd rowCID21329
3rd rowCID12313
4th rowCID3793
5th rowxxxxxxxx
ValueCountFrequency (%)
xxxxxxxx17846
48.2%
cid4370512
 
< 0.1%
cid397911
 
< 0.1%
cid4960110
 
< 0.1%
cid157929
 
< 0.1%
cid239789
 
< 0.1%
cid407979
 
< 0.1%
cid495989
 
< 0.1%
cid620159
 
< 0.1%
cid434288
 
< 0.1%
Other values (11350)19065
51.5%
2025-10-26T00:38:45.719979image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
x142768
48.8%
C19141
 
6.5%
I19141
 
6.5%
D19141
 
6.5%
110838
 
3.7%
210822
 
3.7%
310766
 
3.7%
510537
 
3.6%
410515
 
3.6%
68761
 
3.0%
Other values (12)30232
 
10.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter142813
48.8%
Decimal Number92416
31.6%
Uppercase Letter57428
19.6%
Space Separator5
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
110838
11.7%
210822
11.7%
310766
11.6%
510537
11.4%
410515
11.4%
68761
9.5%
07591
8.2%
87559
8.2%
97550
8.2%
77477
8.1%
Lowercase Letter
ValueCountFrequency (%)
x142768
> 99.9%
r15
 
< 0.1%
e10
 
< 0.1%
o5
 
< 0.1%
f5
 
< 0.1%
a5
 
< 0.1%
l5
 
< 0.1%
Uppercase Letter
ValueCountFrequency (%)
C19141
33.3%
I19141
33.3%
D19141
33.3%
N5
 
< 0.1%
Space Separator
ValueCountFrequency (%)
5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin200241
68.4%
Common92421
31.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
x142768
71.3%
C19141
 
9.6%
I19141
 
9.6%
D19141
 
9.6%
r15
 
< 0.1%
e10
 
< 0.1%
N5
 
< 0.1%
o5
 
< 0.1%
f5
 
< 0.1%
a5
 
< 0.1%
Common
ValueCountFrequency (%)
110838
11.7%
210822
11.7%
310766
11.6%
510537
11.4%
410515
11.4%
68761
9.5%
07591
8.2%
87559
8.2%
97550
8.2%
77477
8.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII292662
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
x142768
48.8%
C19141
 
6.5%
I19141
 
6.5%
D19141
 
6.5%
110838
 
3.7%
210822
 
3.7%
310766
 
3.7%
510537
 
3.6%
410515
 
3.6%
68761
 
3.0%
Other values (12)30232
 
10.3%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size289.1 KiB
Gift Vouchers/Coupons
12637 
Credit/Debit Card Offers
12274 
Without Offers
12081 

Length

Max length24
Median length21
Mean length19.709316
Min length14

Characters and Unicode

Total characters729087
Distinct characters23
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGift Vouchers/Coupons
2nd rowGift Vouchers/Coupons
3rd rowGift Vouchers/Coupons
4th rowGift Vouchers/Coupons
5th rowCredit/Debit Card Offers

Common Values

ValueCountFrequency (%)
Gift Vouchers/Coupons12637
34.2%
Credit/Debit Card Offers12274
33.2%
Without Offers12081
32.7%

Length

2025-10-26T00:38:46.217765image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-26T00:38:46.648117image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
offers24355
28.2%
gift12637
14.7%
vouchers/coupons12637
14.7%
credit/debit12274
14.2%
card12274
14.2%
without12081
14.0%

Most occurring characters

ValueCountFrequency (%)
r61540
 
8.4%
e61540
 
8.4%
f61347
 
8.4%
t61347
 
8.4%
o49992
 
6.9%
s49629
 
6.8%
49266
 
6.8%
i49266
 
6.8%
u37355
 
5.1%
C37185
 
5.1%
Other values (13)210620
28.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter543741
74.6%
Uppercase Letter111169
 
15.2%
Space Separator49266
 
6.8%
Other Punctuation24911
 
3.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r61540
11.3%
e61540
11.3%
f61347
11.3%
t61347
11.3%
o49992
9.2%
s49629
9.1%
i49266
9.1%
u37355
6.9%
h24718
 
4.5%
d24548
 
4.5%
Other values (5)62459
11.5%
Uppercase Letter
ValueCountFrequency (%)
C37185
33.4%
O24355
21.9%
G12637
 
11.4%
V12637
 
11.4%
D12274
 
11.0%
W12081
 
10.9%
Space Separator
ValueCountFrequency (%)
49266
100.0%
Other Punctuation
ValueCountFrequency (%)
/24911
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin654910
89.8%
Common74177
 
10.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
r61540
 
9.4%
e61540
 
9.4%
f61347
 
9.4%
t61347
 
9.4%
o49992
 
7.6%
s49629
 
7.6%
i49266
 
7.5%
u37355
 
5.7%
C37185
 
5.7%
h24718
 
3.8%
Other values (11)160991
24.6%
Common
ValueCountFrequency (%)
49266
66.4%
/24911
33.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII729087
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r61540
 
8.4%
e61540
 
8.4%
f61347
 
8.4%
t61347
 
8.4%
o49992
 
6.9%
s49629
 
6.8%
49266
 
6.8%
i49266
 
6.8%
u37355
 
5.1%
C37185
 
5.1%
Other values (13)210620
28.9%
Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size289.1 KiB
Desktop
13913 
Smartphone
13876 
?
5393 
Both
3810 

Length

Max length10
Median length7
Mean length6.941609
Min length1

Characters and Unicode

Total characters256784
Distinct characters15
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row?
2nd rowDesktop
3rd rowDesktop
4th rowDesktop
5th rowSmartphone

Common Values

ValueCountFrequency (%)
Desktop13913
37.6%
Smartphone13876
37.5%
?5393
 
14.6%
Both3810
 
10.3%

Length

2025-10-26T00:38:47.143630image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-26T00:38:47.578767image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
desktop13913
37.6%
smartphone13876
37.5%
5393
 
14.6%
both3810
 
10.3%

Most occurring characters

ValueCountFrequency (%)
t31599
12.3%
o31599
12.3%
e27789
10.8%
p27789
10.8%
h17686
 
6.9%
D13913
 
5.4%
s13913
 
5.4%
k13913
 
5.4%
S13876
 
5.4%
m13876
 
5.4%
Other values (5)50831
19.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter219792
85.6%
Uppercase Letter31599
 
12.3%
Other Punctuation5393
 
2.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t31599
14.4%
o31599
14.4%
e27789
12.6%
p27789
12.6%
h17686
8.0%
s13913
6.3%
k13913
6.3%
m13876
6.3%
a13876
6.3%
r13876
6.3%
Uppercase Letter
ValueCountFrequency (%)
D13913
44.0%
S13876
43.9%
B3810
 
12.1%
Other Punctuation
ValueCountFrequency (%)
?5393
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin251391
97.9%
Common5393
 
2.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
t31599
12.6%
o31599
12.6%
e27789
11.1%
p27789
11.1%
h17686
 
7.0%
D13913
 
5.5%
s13913
 
5.5%
k13913
 
5.5%
S13876
 
5.5%
m13876
 
5.5%
Other values (4)45438
18.1%
Common
ValueCountFrequency (%)
?5393
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII256784
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t31599
12.3%
o31599
12.3%
e27789
10.8%
p27789
10.8%
h17686
 
6.9%
D13913
 
5.4%
s13913
 
5.4%
k13913
 
5.4%
S13876
 
5.4%
m13876
 
5.4%
Other values (5)50831
19.8%

internet_option
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size289.1 KiB
Wi-Fi
12413 
Mobile_Data
12343 
Fiber_Optic
12236 

Length

Max length11
Median length11
Mean length8.9866458
Min length5

Characters and Unicode

Total characters332434
Distinct characters17
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWi-Fi
2nd rowMobile_Data
3rd rowWi-Fi
4th rowMobile_Data
5th rowMobile_Data

Common Values

ValueCountFrequency (%)
Wi-Fi12413
33.6%
Mobile_Data12343
33.4%
Fiber_Optic12236
33.1%

Length

2025-10-26T00:38:48.072101image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-26T00:38:48.505650image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
wi-fi12413
33.6%
mobile_data12343
33.4%
fiber_optic12236
33.1%

Most occurring characters

ValueCountFrequency (%)
i61641
18.5%
a24686
 
7.4%
F24649
 
7.4%
e24579
 
7.4%
t24579
 
7.4%
_24579
 
7.4%
b24579
 
7.4%
W12413
 
3.7%
-12413
 
3.7%
l12343
 
3.7%
Other values (7)85973
25.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter221458
66.6%
Uppercase Letter73984
 
22.3%
Connector Punctuation24579
 
7.4%
Dash Punctuation12413
 
3.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i61641
27.8%
a24686
11.1%
e24579
 
11.1%
t24579
 
11.1%
b24579
 
11.1%
l12343
 
5.6%
o12343
 
5.6%
r12236
 
5.5%
p12236
 
5.5%
c12236
 
5.5%
Uppercase Letter
ValueCountFrequency (%)
F24649
33.3%
W12413
16.8%
D12343
16.7%
M12343
16.7%
O12236
16.5%
Connector Punctuation
ValueCountFrequency (%)
_24579
100.0%
Dash Punctuation
ValueCountFrequency (%)
-12413
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin295442
88.9%
Common36992
 
11.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
i61641
20.9%
a24686
8.4%
F24649
 
8.3%
e24579
 
8.3%
t24579
 
8.3%
b24579
 
8.3%
W12413
 
4.2%
l12343
 
4.2%
o12343
 
4.2%
D12343
 
4.2%
Other values (5)61287
20.7%
Common
ValueCountFrequency (%)
_24579
66.4%
-12413
33.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII332434
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i61641
18.5%
a24686
 
7.4%
F24649
 
7.4%
e24579
 
7.4%
t24579
 
7.4%
_24579
 
7.4%
b24579
 
7.4%
W12413
 
3.7%
-12413
 
3.7%
l12343
 
3.7%
Other values (7)85973
25.9%
Distinct30101
Distinct (%)81.4%
Missing0
Missing (%)0.0%
Memory size289.1 KiB
Minimum2025-10-26 00:00:00
Maximum2025-10-26 23:59:59
Invalid dates0
Invalid dates (%)0.0%
2025-10-26T00:38:48.953223image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-26T00:38:49.415118image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

days_since_last_login
Real number (ℝ)

Distinct27
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-41.915576
Minimum-999
Maximum26
Zeros0
Zeros (%)0.0%
Negative1999
Negative (%)5.4%
Memory size289.1 KiB
2025-10-26T00:38:49.846781image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-999
5-th percentile-999
Q18
median12
Q316
95-th percentile22
Maximum26
Range1025
Interquartile range (IQR)8

Descriptive statistics

Standard deviation228.8199
Coefficient of variation (CV)-5.4590661
Kurtosis13.545985
Mean-41.915576
Median Absolute Deviation (MAD)4
Skewness-3.9413558
Sum-1550541
Variance52358.547
MonotonicityNot monotonic
2025-10-26T00:38:50.390860image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
122380
 
6.4%
132373
 
6.4%
142307
 
6.2%
152278
 
6.2%
112262
 
6.1%
102091
 
5.7%
162068
 
5.6%
-9991999
 
5.4%
91863
 
5.0%
171747
 
4.7%
Other values (17)15624
42.2%
ValueCountFrequency (%)
-9991999
5.4%
1328
 
0.9%
2613
 
1.7%
3852
2.3%
4998
2.7%
51234
3.3%
61257
3.4%
71442
3.9%
81571
4.2%
91863
5.0%
ValueCountFrequency (%)
2682
 
0.2%
25203
 
0.5%
24471
 
1.3%
23727
2.0%
22895
2.4%
211015
2.7%
201184
3.2%
191308
3.5%
181444
3.9%
171747
4.7%

avg_time_spent
Real number (ℝ)

Distinct25961
Distinct (%)70.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean243.47233
Minimum-2814.1091
Maximum3235.5785
Zeros0
Zeros (%)0.0%
Negative1719
Negative (%)4.6%
Memory size289.1 KiB
2025-10-26T00:38:50.804666image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-2814.1091
5-th percentile30.15
Q160.1025
median161.765
Q3356.515
95-th percentile1031.0767
Maximum3235.5785
Range6049.6876
Interquartile range (IQR)296.4125

Descriptive statistics

Standard deviation398.28915
Coefficient of variation (CV)1.6358703
Kurtosis5.0039153
Mean243.47233
Median Absolute Deviation (MAD)122.88
Skewness0.53962402
Sum9006528.6
Variance158634.25
MonotonicityNot monotonic
2025-10-26T00:38:51.706831image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
34.121
 
0.1%
34.7120
 
0.1%
33.6820
 
0.1%
34.3319
 
0.1%
31.4918
 
< 0.1%
33.2818
 
< 0.1%
32.9118
 
< 0.1%
30.5618
 
< 0.1%
33.7118
 
< 0.1%
32.9617
 
< 0.1%
Other values (25951)36805
99.5%
ValueCountFrequency (%)
-2814.109111
< 0.1%
-2281.2365261
< 0.1%
-2096.5806811
< 0.1%
-2093.1216061
< 0.1%
-2034.801881
< 0.1%
-2012.2673741
< 0.1%
-1960.4791691
< 0.1%
-1941.0354191
< 0.1%
-1918.4863391
< 0.1%
-1913.4051541
< 0.1%
ValueCountFrequency (%)
3235.5785211
< 0.1%
3040.411
< 0.1%
2899.661
< 0.1%
2861.231
< 0.1%
2770.561
< 0.1%
2766.751
< 0.1%
2747.891341
< 0.1%
2732.71
< 0.1%
2722.0777941
< 0.1%
2705.7566081
< 0.1%

avg_transaction_value
Real number (ℝ)

Distinct36894
Distinct (%)99.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29271.194
Minimum800.46
Maximum99914.05
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size289.1 KiB
2025-10-26T00:38:52.119421image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum800.46
5-th percentile3468.9665
Q114177.54
median27554.485
Q340855.11
95-th percentile67338.889
Maximum99914.05
Range99113.59
Interquartile range (IQR)26677.57

Descriptive statistics

Standard deviation19444.806
Coefficient of variation (CV)0.66429836
Kurtosis1.428287
Mean29271.194
Median Absolute Deviation (MAD)13336.775
Skewness1.0110272
Sum1.0828 × 109
Variance3.7810049 × 108
MonotonicityNot monotonic
2025-10-26T00:38:52.549575image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14176.972
 
< 0.1%
7282.582
 
< 0.1%
30126.022
 
< 0.1%
21244.032
 
< 0.1%
23142.512
 
< 0.1%
35460.382
 
< 0.1%
9341.332
 
< 0.1%
34143.62
 
< 0.1%
3432.732
 
< 0.1%
6801.072
 
< 0.1%
Other values (36884)36972
99.9%
ValueCountFrequency (%)
800.461
< 0.1%
804.341
< 0.1%
806.221
< 0.1%
806.711
< 0.1%
813.821
< 0.1%
815.221
< 0.1%
821.831
< 0.1%
822.71
< 0.1%
823.491
< 0.1%
823.681
< 0.1%
ValueCountFrequency (%)
99914.051
< 0.1%
99861.471
< 0.1%
99858.021
< 0.1%
99819.191
< 0.1%
99810.831
< 0.1%
99805.521
< 0.1%
99803.531
< 0.1%
99795.751
< 0.1%
99742.631
< 0.1%
99730.171
< 0.1%
Distinct1654
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Memory size289.1 KiB
2025-10-26T00:38:53.115929image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length21
Median length4
Mean length4.494215
Min length3

Characters and Unicode

Total characters166250
Distinct characters15
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1623 ?
Unique (%)4.4%

Sample

1st row17.0
2nd row10.0
3rd row22.0
4th row6.0
5th row16.0
ValueCountFrequency (%)
error3522
 
9.5%
13.01394
 
3.8%
19.01365
 
3.7%
8.01361
 
3.7%
14.01355
 
3.7%
17.01349
 
3.6%
6.01336
 
3.6%
10.01334
 
3.6%
18.01331
 
3.6%
12.01327
 
3.6%
Other values (1644)21318
57.6%
2025-10-26T00:38:54.030294image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
036699
22.1%
.33470
20.1%
118719
11.3%
216061
9.7%
r10566
 
6.4%
56508
 
3.9%
76331
 
3.8%
66312
 
3.8%
86269
 
3.8%
96258
 
3.8%
Other values (5)19057
11.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number114487
68.9%
Other Punctuation33470
 
20.1%
Lowercase Letter14088
 
8.5%
Uppercase Letter3522
 
2.1%
Dash Punctuation683
 
0.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
036699
32.1%
118719
16.4%
216061
14.0%
56508
 
5.7%
76331
 
5.5%
66312
 
5.5%
86269
 
5.5%
96258
 
5.5%
35692
 
5.0%
45638
 
4.9%
Lowercase Letter
ValueCountFrequency (%)
r10566
75.0%
o3522
 
25.0%
Other Punctuation
ValueCountFrequency (%)
.33470
100.0%
Uppercase Letter
ValueCountFrequency (%)
E3522
100.0%
Dash Punctuation
ValueCountFrequency (%)
-683
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common148640
89.4%
Latin17610
 
10.6%

Most frequent character per script

Common
ValueCountFrequency (%)
036699
24.7%
.33470
22.5%
118719
12.6%
216061
10.8%
56508
 
4.4%
76331
 
4.3%
66312
 
4.2%
86269
 
4.2%
96258
 
4.2%
35692
 
3.8%
Other values (2)6321
 
4.3%
Latin
ValueCountFrequency (%)
r10566
60.0%
E3522
 
20.0%
o3522
 
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII166250
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
036699
22.1%
.33470
20.1%
118719
11.3%
216061
9.7%
r10566
 
6.4%
56508
 
3.9%
76331
 
3.8%
66312
 
3.8%
86269
 
3.8%
96258
 
3.8%
Other values (5)19057
11.5%

points_in_wallet
Real number (ℝ)

High correlation 

Distinct23699
Distinct (%)64.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean687.88161
Minimum-760.66124
Maximum2069.0698
Zeros0
Zeros (%)0.0%
Negative136
Negative (%)0.4%
Memory size289.1 KiB
2025-10-26T00:38:54.384320image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-760.66124
5-th percentile351.82615
Q1624.35
median697.62
Q3757.0025
95-th percentile1028.8753
Maximum2069.0698
Range2829.731
Interquartile range (IQR)132.6525

Descriptive statistics

Standard deviation184.83801
Coefficient of variation (CV)0.26870614
Kurtosis5.1849907
Mean687.88161
Median Absolute Deviation (MAD)65.685
Skewness-0.10049675
Sum25446117
Variance34165.091
MonotonicityNot monotonic
2025-10-26T00:38:54.736842image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
697.623446
 
9.3%
705.079
 
< 0.1%
780.928
 
< 0.1%
771.757
 
< 0.1%
710.697
 
< 0.1%
760.547
 
< 0.1%
620.576
 
< 0.1%
719.396
 
< 0.1%
783.426
 
< 0.1%
734.46
 
< 0.1%
Other values (23689)33484
90.5%
ValueCountFrequency (%)
-760.66123631
< 0.1%
-549.35749771
< 0.1%
-506.25671581
< 0.1%
-483.85640061
< 0.1%
-471.5770091
< 0.1%
-469.02039881
< 0.1%
-445.28845721
< 0.1%
-424.67052481
< 0.1%
-412.44168781
< 0.1%
-405.26703551
< 0.1%
ValueCountFrequency (%)
2069.0697611
< 0.1%
1816.9336961
< 0.1%
1780.7201731
< 0.1%
1763.3515941
< 0.1%
1759.0025321
< 0.1%
1755.4555121
< 0.1%
1755.0946931
< 0.1%
1751.3041951
< 0.1%
1750.6115621
< 0.1%
1736.3325941
< 0.1%

used_special_discount
Boolean

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size36.3 KiB
True
20342 
False
16650 
ValueCountFrequency (%)
True20342
55.0%
False16650
45.0%
2025-10-26T00:38:55.040318image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

offer_application_preference
Boolean

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size36.3 KiB
True
20440 
False
16552 
ValueCountFrequency (%)
True20440
55.3%
False16552
44.7%
2025-10-26T00:38:55.289414image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

past_complaint
Boolean

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size36.3 KiB
False
18602 
True
18390 
ValueCountFrequency (%)
False18602
50.3%
True18390
49.7%
2025-10-26T00:38:55.543749image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

complaint_status
Categorical

High correlation 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size289.1 KiB
Not Applicable
18602 
Unsolved
4644 
Solved
4619 
Solved in Follow-up
4577 
No Information Available
4550 

Length

Max length24
Median length14
Mean length14.09648
Min length6

Characters and Unicode

Total characters521457
Distinct characters26
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNot Applicable
2nd rowSolved
3rd rowSolved in Follow-up
4th rowUnsolved
5th rowSolved

Common Values

ValueCountFrequency (%)
Not Applicable18602
50.3%
Unsolved4644
 
12.6%
Solved4619
 
12.5%
Solved in Follow-up4577
 
12.4%
No Information Available4550
 
12.3%

Length

2025-10-26T00:38:55.874918image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-26T00:38:56.179120image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
not18602
25.2%
applicable18602
25.2%
solved9196
12.5%
unsolved4644
 
6.3%
in4577
 
6.2%
follow-up4577
 
6.2%
no4550
 
6.2%
information4550
 
6.2%
available4550
 
6.2%

Most occurring characters

ValueCountFrequency (%)
l69298
13.3%
o55246
 
10.6%
p41781
 
8.0%
e36992
 
7.1%
36856
 
7.1%
i32279
 
6.2%
a32252
 
6.2%
N23152
 
4.4%
t23152
 
4.4%
A23152
 
4.4%
Other values (16)147297
28.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter410753
78.8%
Uppercase Letter69271
 
13.3%
Space Separator36856
 
7.1%
Dash Punctuation4577
 
0.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
l69298
16.9%
o55246
13.4%
p41781
10.2%
e36992
9.0%
i32279
7.9%
a32252
7.9%
t23152
 
5.6%
b23152
 
5.6%
c18602
 
4.5%
v18390
 
4.5%
Other values (8)59609
14.5%
Uppercase Letter
ValueCountFrequency (%)
N23152
33.4%
A23152
33.4%
S9196
 
13.3%
U4644
 
6.7%
F4577
 
6.6%
I4550
 
6.6%
Space Separator
ValueCountFrequency (%)
36856
100.0%
Dash Punctuation
ValueCountFrequency (%)
-4577
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin480024
92.1%
Common41433
 
7.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
l69298
14.4%
o55246
11.5%
p41781
 
8.7%
e36992
 
7.7%
i32279
 
6.7%
a32252
 
6.7%
N23152
 
4.8%
t23152
 
4.8%
A23152
 
4.8%
b23152
 
4.8%
Other values (14)119568
24.9%
Common
ValueCountFrequency (%)
36856
89.0%
-4577
 
11.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII521457
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
l69298
13.3%
o55246
 
10.6%
p41781
 
8.0%
e36992
 
7.1%
36856
 
7.1%
i32279
 
6.2%
a32252
 
6.2%
N23152
 
4.4%
t23152
 
4.4%
A23152
 
4.4%
Other values (16)147297
28.2%

feedback
Categorical

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size289.1 KiB
Poor Product Quality
6350 
No reason specified
6290 
Too many ads
6279 
Poor Website
6271 
Poor Customer Service
6252 
Other values (4)
5550 

Length

Max length24
Median length21
Mean length17.355455
Min length12

Characters and Unicode

Total characters642013
Distinct characters31
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowProducts always in Stock
2nd rowQuality Customer Care
3rd rowPoor Website
4th rowPoor Website
5th rowPoor Website

Common Values

ValueCountFrequency (%)
Poor Product Quality6350
17.2%
No reason specified6290
17.0%
Too many ads6279
17.0%
Poor Website6271
17.0%
Poor Customer Service6252
16.9%
Reasonable Price1417
 
3.8%
User Friendly Website1391
 
3.8%
Products always in Stock1382
 
3.7%
Quality Customer Care1360
 
3.7%

Length

2025-10-26T00:38:56.600157image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-26T00:38:56.965399image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
poor18873
18.0%
quality7710
 
7.4%
website7662
 
7.3%
customer7612
 
7.3%
product6350
 
6.1%
reason6290
 
6.0%
no6290
 
6.0%
specified6290
 
6.0%
too6279
 
6.0%
many6279
 
6.0%
Other values (11)25035
23.9%

Most occurring characters

ValueCountFrequency (%)
o81027
12.6%
67678
 
10.5%
e62703
 
9.8%
r52318
 
8.1%
s39705
 
6.2%
i38394
 
6.0%
a33516
 
5.2%
t32098
 
5.0%
P28022
 
4.4%
c23073
 
3.6%
Other values (21)183479
28.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter497567
77.5%
Uppercase Letter76768
 
12.0%
Space Separator67678
 
10.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o81027
16.3%
e62703
12.6%
r52318
10.5%
s39705
8.0%
i38394
7.7%
a33516
 
6.7%
t32098
 
6.5%
c23073
 
4.6%
u23054
 
4.6%
d21692
 
4.4%
Other values (10)89987
18.1%
Uppercase Letter
ValueCountFrequency (%)
P28022
36.5%
C8972
 
11.7%
Q7710
 
10.0%
W7662
 
10.0%
S7634
 
9.9%
N6290
 
8.2%
T6279
 
8.2%
R1417
 
1.8%
U1391
 
1.8%
F1391
 
1.8%
Space Separator
ValueCountFrequency (%)
67678
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin574335
89.5%
Common67678
 
10.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
o81027
14.1%
e62703
 
10.9%
r52318
 
9.1%
s39705
 
6.9%
i38394
 
6.7%
a33516
 
5.8%
t32098
 
5.6%
P28022
 
4.9%
c23073
 
4.0%
u23054
 
4.0%
Other values (20)160425
27.9%
Common
ValueCountFrequency (%)
67678
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII642013
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o81027
12.6%
67678
 
10.5%
e62703
 
9.8%
r52318
 
8.1%
s39705
 
6.2%
i38394
 
6.0%
a33516
 
5.2%
t32098
 
5.0%
P28022
 
4.4%
c23073
 
3.6%
Other values (21)183479
28.6%

churn_risk_score
Real number (ℝ)

High correlation 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.4633975
Minimum-1
Maximum5
Zeros0
Zeros (%)0.0%
Negative1163
Negative (%)3.1%
Memory size289.1 KiB
2025-10-26T00:38:57.334189image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile1
Q13
median4
Q35
95-th percentile5
Maximum5
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.4096609
Coefficient of variation (CV)0.40701679
Kurtosis1.299243
Mean3.4633975
Median Absolute Deviation (MAD)1
Skewness-1.1143052
Sum128118
Variance1.9871439
MonotonicityNot monotonic
2025-10-26T00:38:57.578071image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
310424
28.2%
410185
27.5%
59827
26.6%
22741
 
7.4%
12652
 
7.2%
-11163
 
3.1%
ValueCountFrequency (%)
-11163
 
3.1%
12652
 
7.2%
22741
 
7.4%
310424
28.2%
410185
27.5%
59827
26.6%
ValueCountFrequency (%)
59827
26.6%
410185
27.5%
310424
28.2%
22741
 
7.4%
12652
 
7.2%
-11163
 
3.1%

Interactions

2025-10-26T00:38:29.059391image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-26T00:38:20.444909image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-26T00:38:22.053875image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-26T00:38:23.636490image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-26T00:38:25.276070image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-26T00:38:26.906073image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-26T00:38:29.360344image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-26T00:38:20.709781image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-26T00:38:22.307154image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-26T00:38:23.910297image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-26T00:38:25.548791image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-26T00:38:27.162413image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-26T00:38:29.644701image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-26T00:38:20.970649image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-26T00:38:22.545839image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-26T00:38:24.194438image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-26T00:38:25.818640image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-26T00:38:27.542894image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-26T00:38:30.027869image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-26T00:38:21.249086image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-26T00:38:22.831471image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-26T00:38:24.459643image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-26T00:38:26.109293image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-26T00:38:27.936881image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-26T00:38:30.419929image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-26T00:38:21.515804image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-26T00:38:23.098208image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-26T00:38:24.732569image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-26T00:38:26.364191image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-26T00:38:28.346518image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-26T00:38:30.817077image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-26T00:38:21.771750image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-26T00:38:23.368308image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-26T00:38:24.990508image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-26T00:38:26.627165image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-26T00:38:28.707696image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2025-10-26T00:38:57.832110image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ageavg_time_spentavg_transaction_valuechurn_risk_scorecomplaint_statusdays_since_last_loginfeedbackgenderinternet_optionjoined_through_referralmedium_of_operationmembership_categoryoffer_application_preferencepast_complaintpoints_in_walletpreferred_offer_typesregion_categoryused_special_discount
age1.0000.003-0.0010.0040.000-0.0030.0060.0000.0140.0030.0080.0000.0000.012-0.0010.0070.0000.000
avg_time_spent0.0031.0000.019-0.0290.002-0.1000.0210.0000.0100.0880.2090.0040.1040.0000.0110.0070.0120.113
avg_transaction_value-0.0010.0191.000-0.2010.000-0.0050.2440.0000.0020.0330.0230.1310.0350.0000.1060.0390.0240.000
churn_risk_score0.004-0.029-0.2011.0000.0000.0160.4410.0000.0000.0470.0270.4130.0500.011-0.5480.0640.0340.006
complaint_status0.0000.0020.0000.0001.0000.0130.0000.0000.0050.0050.0000.0000.0111.0000.0000.0070.0000.004
days_since_last_login-0.003-0.100-0.0050.0160.0131.0000.0120.0080.0000.0080.0000.0000.0000.000-0.0020.0040.0000.000
feedback0.0060.0210.2440.4410.0000.0121.0000.0030.0050.0470.0280.1880.0490.0100.0830.0640.0330.008
gender0.0000.0000.0000.0000.0000.0080.0031.0000.0000.0000.0020.0000.0000.0040.0000.0060.0000.000
internet_option0.0140.0100.0020.0000.0050.0000.0050.0001.0000.0040.0000.0030.0000.0000.0000.0000.0000.000
joined_through_referral0.0030.0880.0330.0470.0050.0080.0470.0000.0041.0000.0440.0220.0210.0000.0060.0000.0000.019
medium_of_operation0.0080.2090.0230.0270.0000.0000.0280.0020.0000.0441.0000.0130.0490.0050.0000.0000.0000.063
membership_category0.0000.0040.1310.4130.0000.0000.1880.0000.0030.0220.0131.0000.0110.0050.2050.0240.0130.004
offer_application_preference0.0000.1040.0350.0500.0110.0000.0490.0000.0000.0210.0490.0111.0000.0050.0030.0000.0000.814
past_complaint0.0120.0000.0000.0111.0000.0000.0100.0040.0000.0000.0050.0050.0051.0000.0000.0000.0000.005
points_in_wallet-0.0010.0110.106-0.5480.000-0.0020.0830.0000.0000.0060.0000.2050.0030.0001.0000.0110.0060.010
preferred_offer_types0.0070.0070.0390.0640.0070.0040.0640.0060.0000.0000.0000.0240.0000.0000.0111.0000.0040.000
region_category0.0000.0120.0240.0340.0000.0000.0330.0000.0000.0000.0000.0130.0000.0000.0060.0041.0000.000
used_special_discount0.0000.1130.0000.0060.0040.0000.0080.0000.0000.0190.0630.0040.8140.0050.0100.0000.0001.000

Missing values

2025-10-26T00:38:32.027256image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2025-10-26T00:38:33.413978image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

customer_idNameagegendersecurity_noregion_categorymembership_categoryjoining_datejoined_through_referralreferral_idpreferred_offer_typesmedium_of_operationinternet_optionlast_visit_timedays_since_last_loginavg_time_spentavg_transaction_valueavg_frequency_login_dayspoints_in_walletused_special_discountoffer_application_preferencepast_complaintcomplaint_statusfeedbackchurn_risk_score
0fffe4300490044003600300030003800Pattie Morrisey18FXW0DQ7HVillagePlatinum Membership2017-08-17NoxxxxxxxxGift Vouchers/Coupons?Wi-Fi16:08:0217300.6353005.2517.0781.75YesYesNoNot ApplicableProducts always in Stock2
1fffe43004900440032003100300035003700Traci Peery32F5K0N3X1CityPremium Membership2017-08-28?CID21329Gift Vouchers/CouponsDesktopMobile_Data12:38:1316306.3412838.3810.0697.62YesNoYesSolvedQuality Customer Care1
2fffe4300490044003100390032003600Merideth Mcmeen44F1F2TCL3TownNo Membership2016-11-11YesCID12313Gift Vouchers/CouponsDesktopWi-Fi22:53:2114516.1621027.0022.0500.69NoYesYesSolved in Follow-upPoor Website5
3fffe43004900440036003000330031003600Eufemia Cardwell37MVJGJ33NCityNo Membership2016-10-29YesCID3793Gift Vouchers/CouponsDesktopMobile_Data15:57:501153.2725239.566.0567.66NoYesYesUnsolvedPoor Website5
4fffe43004900440031003900350030003600Meghan Kosak31FSVZXCWBCityNo Membership2017-09-12NoxxxxxxxxCredit/Debit Card OffersSmartphoneMobile_Data15:46:4420113.1324483.6616.0663.06NoYesYesSolvedPoor Website5
5fffe43004900440036003300320035003300Leslie Browder13MPSG1LGFCityGold Membership2016-01-08NoxxxxxxxxGift Vouchers/Coupons?Wi-Fi06:46:0723433.6213884.7724.0722.27YesNoYesUnsolvedNo reason specified3
6fffe43004900440031003100360037003900Bridget Balog21MR3CX1EATownGold Membership2015-03-19YesCID24708Gift Vouchers/CouponsDesktopMobile_Data11:40:041055.388982.5028.0756.21YesNoYesSolved in Follow-upNo reason specified3
7fffe4300490044003800300035003800Herma Torgeson42M4UJ1551TownNo Membership2016-07-12?CID56614Credit/Debit Card OffersBothFiber_Optic07:52:4319429.1144554.8224.0568.08NoYesYesUnsolvedPoor Product Quality5
8fffe43004900440033003300330032003200Pattie Helmers44M0481QNQVillageSilver Membership2016-12-14NoxxxxxxxxWithout OffersSmartphoneFiber_Optic06:50:1015191.0718362.3120.0697.62YesNoYesSolved in Follow-upPoor Customer Service3
9fffe43004900440032003000340038003300Shaquana Leech45FZHP4MCRTownNo Membership2016-11-30NoxxxxxxxxGift Vouchers/Coupons?Wi-Fi19:10:161097.3119244.1628.0706.23NoYesYesNo Information AvailablePoor Customer Service4
customer_idNameagegendersecurity_noregion_categorymembership_categoryjoining_datejoined_through_referralreferral_idpreferred_offer_typesmedium_of_operationinternet_optionlast_visit_timedays_since_last_loginavg_time_spentavg_transaction_valueavg_frequency_login_dayspoints_in_walletused_special_discountoffer_application_preferencepast_complaintcomplaint_statusfeedbackchurn_risk_score
36982fffe43004900440033003600330033003800Leslie Bruneau45FI2TAL7NTownPremium Membership2016-08-31NoxxxxxxxxGift Vouchers/Coupons?Wi-Fi08:30:411034.93000041558.9319.0703.030000YesNoNoNot ApplicablePoor Product Quality3
36983fffe43004900440032003300370030003700Faustina Balog45MPU0XLQYTownBasic Membership2016-08-30YesCID45477Without OffersSmartphoneWi-Fi10:53:31949.33000045358.4911.0242.979625YesNoNoNot ApplicablePoor Customer Service5
36984fffe43004900440035003800320035003300Hilary Ortego51MLM92BDSTownGold Membership2016-10-07NoxxxxxxxxWithout OffersDesktopFiber_Optic15:41:3624312.33000063446.712.0778.700000NoYesNoNot ApplicableProducts always in Stock1
36985fffe4300490044003800310034003500Dwain Cann12FGWAHGJYVillagePremium Membership2016-10-25NoxxxxxxxxGift Vouchers/CouponsDesktopFiber_Optic03:30:1713418.38000056397.217.0725.890000YesYesYesUnsolvedProducts always in Stock2
36986fffe43004900440034003900300036003500Marlena Chastain27M8X0LUUSTownPlatinum Membership2015-09-07YesCID15800Credit/Debit Card OffersDesktopMobile_Data05:29:1913135.8300008225.6816.0748.570000YesNoNoNot ApplicableNo reason specified3
36987fffe43004900440035003500390036003100Cuc Tarr46F6F51HFOTownBasic Membership2017-09-21NoxxxxxxxxCredit/Debit Card OffersDesktopWi-Fi04:14:052-650.68275927277.686.0639.510000NoYesYesNo Information AvailableNo reason specified4
36988fffe43004900440033003500380036003600Jenni Stronach29F21KSM8YTownBasic Membership2016-06-27NoxxxxxxxxWithout OffersSmartphoneWi-Fi23:18:3113-638.12342111069.7128.0527.990000YesNoNoNot ApplicablePoor Customer Service5
36989fffe4300490044003500330034003100Luciana Kinch23FXK1IM9HTownBasic Membership2016-09-11YesCID3838Gift Vouchers/CouponsDesktopWi-Fi03:50:2512154.94000038127.56Error680.470000NoYesYesUnsolvedPoor Website4
36990fffe43004900440031003200390039003000Tawana Ardoin53MK6VTP1ZVillagePlatinum Membership2017-06-15NoxxxxxxxxGift Vouchers/CouponsSmartphoneMobile_Data09:50:0315482.6100002378.8620.0197.264414YesYesNoNot ApplicableNo reason specified3
36991fffe43004900440033003600340034003200Verlene Beaulieu35MLBX0GLRTownSilver Membership2015-10-23NoxxxxxxxxGift Vouchers/CouponsDesktopMobile_Data01:39:521579.1800002189.68Error719.970000YesNoNoNot ApplicableQuality Customer Care2